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Demonstrating chart-plot: Closing the Last Mile of Academic Chart Generation
arXiv:2606.09174v1 Announce Type: new Abstract: Large language models can translate a researcher's intent into runnable matplotlib code, yet the resulting chart rarely lands in a paper without multiple rounds of manual revision. We argue that the open problem is not chart code generation but chart publication: making the output look like a top-venue figure, survive the target layout, and respond to precise author edits. We present chart-plot, an agentic harness that closes this last mile...
Channel Chart Location Privacy Based on Geo-Indistinguishability
Announce Type: new Abstract: Channel charting enables location-based services (LBSs) without requiring explicit position information by using pseudo-locations from the channel chart. While this property implies inherent privacy advantages, it does not provide formal privacy guarantees. In this work, we address location privacy in channel charting referred to as chart location indistinguishability (CLI), which extends geo-indistinguishability (GI) to channel charting representations.
ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation
arXiv:2601.12983v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting efficiency while introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs can generate misleading charts at scale by injecting misleaders into chart designs to induce incorrect interpretations. We also introduce AttackViz, a chart question-answering (QA) dataset...
ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats
arXiv:2606.01348v2 Announce Type: replace Abstract: Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus on narrow chart types and leave diagrammatic structures such as flowcharts and mind maps largely unaddressed, while models produce outputs in incompatible formats, and datasets rarely include the printed or hand-drawn images encountered in practice. To address...
ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats
Announce Type: new Abstract: Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus on narrow chart types and leave diagrammatic structures such as flowcharts and mind maps largely unaddressed, while models produce outputs in incompatible formats, and datasets rarely include the printed or hand-drawn images encountered in practice. To address these issues, we introduce...
Semantic-Structural Alignment for Generative Pictorial Charts
new Abstract: Traditional statistical graphics are precise but often lack the visual appeal, memorability, and engagement of pictorial charts. We present a generative framework for the automated synthesis of pictorial charts that bridges the gap between semantic expression and structural faithfulness. Rather than treating charts merely as images to be stylized, we frame the problem as a dual-conditioned generation task guided by two parallel external control signals: a text prompt capturing...
Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification
arXiv:2606.01679v1 Announce Type: new Abstract: Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by its evidence. Prior work has shown that models perform substantially better at this task when the evidence is a table than when it is a chart of the same underlying data. This raises the question of whether models fail to extract information from charts, or do they extract it but fail to use it...
A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels
arXiv:2606.02232v1 Announce Type: new Abstract: Learning a Markov transition model is not merely conditional density estimation: the learned object must be a valid transition kernel before it is iterated in downstream dynamics. This paper introduces a Doeblin-anchored contrastive chart, a statistical-to-dynamical coordinate framework for learning transition kernels from contrastive objectives. Given a restart law and an anchor strength, the chart mixes the target transition with the restart law.
HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers
arXiv:2606.01132v1 Announce Type: new Abstract: Understanding chart and table images is essential for applying vision-language models (VLMs) to real-world document understanding. While English benchmarks have advanced rapidly, non-English counterparts remain scarce, leaving it unclear whether this progress generalizes across languages.
Publisher Correction: White matter micro- and macrostructure brain charts for the human lifespan
Nature, Published online: 02 June 2026; doi:10.1038/s41586-026-10693-3Publisher Correction: White matter micro- and macrostructure brain charts for the human lifespan